U.S. patent number 10,673,372 [Application Number 15/836,175] was granted by the patent office on 2020-06-02 for cognitively predicting dust deposition on solar photovoltaic modules.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is International Business Machines Corporation. Invention is credited to Vijay Arya, Amar P. Azad, Rashmi Mittal.
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United States Patent |
10,673,372 |
Azad , et al. |
June 2, 2020 |
Cognitively predicting dust deposition on solar photovoltaic
modules
Abstract
Methods, systems, and computer program products for cognitively
predicting dust deposition on solar photovoltaic modules are
provided herein. A computer-implemented method includes deriving,
with respect to solar photovoltaic modules, dust parameters from
image data, and estimating, for a given future time at a current
module orientation, an amount of surface area of the modules that
will be covered by dust and a yield loss of the modules associated
with dust coverage. The method also includes forecasting, for the
given future time at each of one or more modified module
orientations, an amount of surface area of the modules that will be
covered by dust and a yield loss of the modules associated with
dust coverage. Further, the method includes generating an
instruction to change the orientation of at least one of the
modules, and outputting the instruction to at least one actuation
system associated with the modules.
Inventors: |
Azad; Amar P. (Bangalore,
IN), Mittal; Rashmi (New Delhi, IN), Arya;
Vijay (Bangalore, IN) |
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
66697405 |
Appl.
No.: |
15/836,175 |
Filed: |
December 8, 2017 |
Prior Publication Data
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|
|
Document
Identifier |
Publication Date |
|
US 20190181793 A1 |
Jun 13, 2019 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N
15/06 (20130101); H02S 40/10 (20141201); H02S
50/10 (20141201); H02S 10/00 (20130101); H02S
50/15 (20141201); G01N 2015/0096 (20130101); G01N
2015/0693 (20130101) |
Current International
Class: |
H02S
10/00 (20140101); G01N 15/06 (20060101); H02S
50/10 (20140101); H02S 40/10 (20140101); H02S
50/15 (20140101); G01N 15/00 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2017034932 |
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Feb 2017 |
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JP |
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2016208969 |
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Dec 2016 |
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WO |
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Other References
JP-2017034932-A, Machine Translation, Sakurazawa, Feb. 2, 2019
(Year: 2019). cited by examiner .
OpenEI, PV Watts Calculator, https://openei.org/wiki/PVWatts, 2017.
cited by applicant .
Joint Research Centre, Institute for Energy and Transport,
Photovoltaic Geographical Information System (PVGIS),
https://web.archive.org/web/20171204041707/http://re.jrc.ec.europa.eu/pvg-
is/, Dec. 4, 2017. cited by applicant .
Jones et al., Optimized Cleaning Cost and Schedule Based on
Observed Soiling Conditions for Photovoltaic Plants in Central
Saudi Arabia, IEEE Journal of Photovoltaics, vol. 6, No. 3, May
2016. cited by applicant .
Adinoyi et al., Effect of dust accumulation on the power outputs of
solar photovoltaic modules, Renewable Energy, vol. 30, Dec. 2013.
cited by applicant .
Maghami et al., Mathematical Relationship Identification for
Photovoltaic Systems under Dusty Condition, IEEE European Modelling
Symposium 2015. cited by applicant .
Lan et al., "Numerical Study of Sand Deposition and Control by Flat
Solar Panels", Proceedings of the ASME 2012 International
Mechanical Engineering Congress & Exposition IMECE2012 Nov.
9-15, 2012, Houston, Texas, USA. cited by applicant .
Bitsuamlaka et al., "Evaluation of wind loads on solar panel
modules using CFD", Proceedings of the Fifth International
Symposium on Computational Wind Engineering (CWE2010) Chapel Hill,
North Carolina, USA May 23-27, 2010. cited by applicant .
Wang et al., "Modeling of Dust Deposition Affecting Transmittance
of PV Modules", Journal of Clean Energy Technologies, vol. 5, No.
3, May 2017. cited by applicant .
Sayyah et al., "Energy yield loss caused by dust deposition on
photovoltaic panels", Journal of Solar Energy 107(2014) 576-604.
cited by applicant .
Elminir et al., "Effect of dust on the transparent cover of solar
collectors" Journal of Energy Conyers. Manage, 47 (18),
3192-3203,2006. cited by applicant .
Molki, A., Dust affects solar-cell efficiency. Phys. Edu. 45 (5),
456-458, 2010. cited by applicant .
Sarver et al., "A comprehensive review of the impact of dust on the
use of solar energy: History, investigations, results, literature,
and mitigation approaches", Journal of Renewable and Sustainable
Energy Reviews 22 (2013) 698-733. cited by applicant .
Wang et al., "Implementation of dust emission and chemistry into
the Community Multiscale Air Quality modeling system and initial
application to an Asian dust storm episode",Joumal of Atmos. Chem.
Phys., 12, 10209-10237, 2012. cited by applicant .
Sulaiman et al.., "Effects of dust on the performance of PV
panels". Proceedings of World Acad. Sci., Eng. Technol. 58,
588-593, 2011. cited by applicant .
Nahar et al., "Effect of dust on transmittance of glazing materials
for solar collectors under arid zone conditions of India", Journal
of Solar Wind Technol. 7 (2), 237-243, 1990. cited by applicant
.
Kaldellis et al., "Quantifying the decrease of the photovoltaic
panels' energy yield due to phenomena of natural air pollution
disposal". Energy 35 (12), 4862-4869. 2010. cited by
applicant.
|
Primary Examiner: Pillay; Devina
Attorney, Agent or Firm: Ryan, Mason & Lewis, LLP
Claims
What is claimed is:
1. A computer-implemented method, the method comprising: deriving,
with respect to one or more solar photovoltaic modules, multiple
dust parameters from image data; estimating, for a given future
time at a current orientation of each of the one or more solar
photovoltaic modules, (i) an amount of surface area of the one or
more solar photovoltaic modules that will be covered by dust and
(ii) a yield loss of the one or more solar photovoltaic modules
associated with dust coverage, wherein said estimating is based on
the multiple dust parameters and one or more items of input data;
forecasting, for the given future time at each of one or more
orientations for each of the one or more solar photovoltaic
modules, (i) an amount of surface area of the one or more solar
photovoltaic modules that will be covered by dust and (ii) a yield
loss of the one or more solar photovoltaic modules associated with
dust coverage, wherein said forecasting is based on the multiple
dust parameters, the one or more items of input data, and one or
more machine learning techniques; generating an instruction to
change the orientation of at least one of the one or more solar
photovoltaic modules, prior to the given future time, based on said
estimating and said forecasting; and changing, via at least one
actuation system associated with the one or more solar photovoltaic
modules, the orientation of the at least solar photovoltaic module
in accordance with the instruction; wherein the method is carried
out by at least one computing device.
2. The computer-implemented method of claim 1, wherein the multiple
dust parameters comprise dust type and one or more additional dust
parameters.
3. The computer-implemented method of claim 1, wherein the multiple
dust parameters comprise data pertaining to interactions between
(i) dust on the one or more solar photovoltaic modules and (ii)
precipitation.
4. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise input data from one or more
Internet of Things devices.
5. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise ambient weather forecast
data.
6. The computer-implemented method of claim 1, wherein the one or
more items of input data comprises one or more surrounding soil
parameters.
7. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise forecasted wind speed data.
8. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise precipitation data.
9. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise ambient dust forecast data.
10. The computer-implemented method of claim 9, wherein the ambient
dust forecast data comprise one or more local dust transport models
and one or more global dust transport models.
11. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise location data associated with the
one or more solar photovoltaic modules.
12. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise cleaning schedule data associated
with the one or more solar photovoltaic modules.
13. The computer-implemented method of claim 1, wherein the one or
more items of input data comprise historical output data associated
with the one or more solar photovoltaic modules.
14. The computer-implemented method of claim 1, wherein the one or
more orientations comprise one or more tilt angles of at least one
of the one or more solar photovoltaic modules.
15. The computer-implemented method of claim 1, wherein the one or
more orientations comprise one or more rotation positions of at
least one of the one or more solar photovoltaic modules.
16. The computer-implemented method of claim 1, wherein the
instruction reduces dust deposition on the one or more solar
photovoltaic modules and increases received irradiance by the one
or more solar photovoltaic modules.
17. The computer-implemented method of claim 1, comprising:
limiting orientations for at least one of the one or more solar
photovoltaic module based on one or more temporally-based rules
associated with the given future time.
18. A computer program product comprising a non-transitory computer
readable storage medium having program instructions embodied
therewith, the program instructions executable by a computing
device to cause the computing device to: derive, with respect to
one or more solar photovoltaic modules, multiple dust parameters
from image data; estimate, for a given future time at a current
orientation of each of the one or more solar photovoltaic modules,
(i) an amount of surface area of the one or more solar photovoltaic
modules that will be covered by dust and (ii) a yield loss of the
one or more solar photovoltaic modules associated with dust
coverage, wherein said estimating is based on the multiple dust
parameters and one or more items of input data; forecast, for the
given future time at each of one or more orientations for each of
the one or more solar photovoltaic modules, (i) an amount of
surface area of the one or more solar photovoltaic modules that
will be covered by dust and (ii) a yield loss of the one or more
solar photovoltaic modules associated with dust coverage, wherein
said forecasting is based on the multiple dust parameters, the one
or more items of input data, and one or more machine learning
techniques; generate an instruction to change the orientation of at
least one of the one or more solar photovoltaic modules, prior to
the given future time, based on said estimating and said
forecasting; and change, via at least one actuation system
associated with the one or more solar photovoltaic modules, the
orientation of the at least solar photovoltaic module in accordance
with the instruction.
19. A system comprising: a memory; and at least one processor
operably coupled to the memory and configured for: deriving, with
respect to one or more solar photovoltaic modules, multiple dust
parameters from image data; estimating, for a given future time at
a current orientation of each of the one or more solar photovoltaic
modules, (i) an amount of surface area of the one or more solar
photovoltaic modules that will be covered by dust and (ii) a yield
loss of the one or more solar photovoltaic modules associated with
dust coverage, wherein said estimating is based on the multiple
dust parameters and one or more items of input data; forecasting,
for the given future time at each of one or more orientations for
each of the one or more solar photovoltaic modules, (i) an amount
of surface area of the one or more solar photovoltaic modules that
will be covered by dust and (ii) a yield loss of the one or more
solar photovoltaic modules associated with dust coverage, wherein
said forecasting is based on the multiple dust parameters, the one
or more items of input data, and one or more machine learning
techniques; generating an instruction to change the orientation of
at least one of the one or more solar photovoltaic modules, prior
to the given future time, based on said estimating and said
forecasting; and changing, via at least one actuation system
associated with the one or more solar photovoltaic modules, the
orientation of the at least solar photovoltaic module in accordance
with the instruction.
Description
FIELD
The present application generally relates to information
technology, and, more particularly, to photonic energy device
management.
BACKGROUND
Dust is a common problem worldwide which impacts the yield and
revenue generated by solar photovoltaic (PV) modules and solar
farms. Dust deposition can degrade PV output in a nonlinear manner,
and geographies with high solar potential are commonly arid and
prone to increased amounts of dust. Also, various types of dust
depositions can cause different levels of power degradations and
minimize the intensity of the irradiance incident on a solar
photovoltaic module.
SUMMARY
In one embodiment of the present invention, techniques for
cognitively predicting dust deposition on solar photovoltaic
modules are provided. An exemplary computer-implemented method can
include deriving, with respect to one or more solar photovoltaic
modules, multiple dust parameters from image data, and estimating,
for a given future time at a current orientation of each of the one
or more solar photovoltaic modules, (i) an amount of surface area
of the one or more solar photovoltaic modules that will be covered
by dust and (ii) a yield loss of the one or more solar photovoltaic
modules associated with dust coverage, wherein said estimating is
based on the multiple dust parameters and one or more items of
input data. Such an embodiment also includes forecasting, for the
given future time at each of one or more modified orientations for
each of the one or more solar photovoltaic modules, (i) an amount
of surface area of the one or more solar photovoltaic modules that
will be covered by dust and (ii) a yield loss of the one or more
solar photovoltaic modules associated with dust coverage, wherein
said forecasting is based on the multiple dust parameters, the one
or more items of input data, and one or more machine learning
techniques. Further, such an embodiment includes generating an
instruction to change the orientation of at least one of the one or
more solar photovoltaic modules, prior to the given future time,
based on said estimating and said forecasting, and outputting the
instruction to at least one actuation system associated with the
one or more solar photovoltaic modules.
In yet another embodiment of the invention, a system can include a
solar photovoltaic module and one or more configurable reflective
surfaces that (i) collect direct solar radiation and diffuse solar
radiation and (ii) distribute the collected direct solar radiation
and the collected diffuse solar radiation across one or more
portions of the solar photovoltaic module. Also, in such an
embodiment, each one of the plurality of configurable reflective
surfaces is physically connected to the solar photovoltaic module
at an angle that is variable in relation to the surface of the
solar photovoltaic module, and at least one variable pertaining to
each one of the plurality of configurable reflective surfaces is
configurable, wherein the at least one variable relates to
reflective surface orientation. Additionally, such an embodiment
includes a controller, wherein said controller comprises at least a
memory and a processor coupled to the memory, and wherein the
controller modulates an amount of thermal output and/or electrical
power output generated by the solar photovoltaic module, in
response to at least one forecast pertaining to (i) an amount of
surface area of the solar photovoltaic module that will be covered
by dust at a given future time in connection with multiple
configurations of the at least one variable and (ii) a yield loss
of the one or more solar photovoltaic modules associated with dust
coverage, by transmitting a signal to adjust the at least one
variable in response to the at least one forecast.
Another embodiment of the invention or elements thereof can be
implemented in the form of a computer program product tangibly
embodying computer readable instructions which, when implemented,
cause a computer to carry out a plurality of method steps, as
described herein. Furthermore, another embodiment of the invention
or elements thereof can be implemented in the form of a system
including a memory and at least one processor that is coupled to
the memory and configured to perform noted method steps. Yet
further, another embodiment of the invention or elements thereof
can be implemented in the form of means for carrying out the method
steps described herein, or elements thereof; the means can include
hardware module(s) or a combination of hardware and software
modules, wherein the software modules are stored in a tangible
computer-readable storage medium (or multiple such media).
These and other objects, features and advantages of the present
invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a diagram illustrating system architecture, according to
an exemplary embodiment of the invention;
FIG. 2 is a diagram illustrating dust prediction components,
according to an exemplary embodiment of the invention;
FIG. 3 is a diagram illustrating dust prediction components,
according to an exemplary embodiment of the invention;
FIG. 4 is a diagram illustrating dust prediction components,
according to an exemplary embodiment of the invention;
FIG. 5 is a diagram illustrating elements for forecasting solar
photovoltaic module dust, according to an exemplary embodiment of
the invention;
FIG. 6 is a flow diagram illustrating techniques according to an
embodiment of the invention;
FIG. 7 is a system diagram of an exemplary computer system on which
at least one embodiment of the invention can be implemented;
FIG. 8 depicts a cloud computing environment according to an
embodiment of the present invention; and
FIG. 9 depicts abstraction model layers according to an embodiment
of the present invention.
DETAILED DESCRIPTION
As described herein, an embodiment of the present invention
includes cognitively predicting dust deposition on solar
photovoltaic modules (also referred to herein as solar arrays or
solar panels). At least one embodiment of the invention includes
collecting weather and solar farm data from Internet of Things
(IoT) devices for cognitively predicting dust deposition on solar
arrays, and accordingly changing orientations of individual and/or
multiple solar arrays in the farm, to minimize dust deposition and
maximizing power generation/yield. Such an embodiment can also
include deriving panel dust parameters (such as dust type, dust
thickness, interaction of panel dust with precipitation, etc.) from
image data and estimating the panel area covered by dust. By way of
example, one or more embodiments of the invention can include
implementing hybrid physics-based and machine learning models,
which take into account data such as surrounding soil parameters,
forecasted wind speeds and precipitation, along with local and
global dust transport models, to estimate dust deposition.
Further, at least one embodiment of the invention includes using
machine learning techniques to forecast panel dust parameters and
power/yield losses at different tilt angles and changing panel
orientations (within a solar farm, for example) to minimize dust
deposition and maximize yield/electricity generation.
As further detailed herein, factors impacting dust accumulation on
panels can include environmental factors, dust-related factors, and
location and installation factors. Environmental factors can
include wind movement, wind direction, temperature, irradiation,
air pollution, air pressure, dust storms, volcanoes, snow,
humidity, etc. Dust-related factors can include dust type, such as
soil and sand, clay, bacteria, carbon, etc. Additionally, location
and installation factors can include, for example, sandy areas,
industrial areas, glass material(s), orientation, height, tilt
angle, flat or roughened surface, latitude and longitude, etc.
FIG. 1 is a diagram illustrating system architecture, according to
an embodiment of the invention. By way of illustration, FIG. 1
depicts a predictive dust IoT tracker system 102 (which can be
located on premise or in the cloud) and actuator systems 104a,
104b, 104c (herein collectively referred to as actuator systems
104) associated with one or more respective solar photovoltaic
modules. The prediction system 102 processes, from one or more data
sources, input data such as, for example, ambient weather
forecasts, ambient dust forecasts, location-specific data solar
photovoltaic module (panel) data, historical panel image data,
historical cleaning schedule data, and/or historical PV output
data, and leverages machine learning and physics-based dust
dispersion models to forecast dust deposition-based power losses at
different panel tilt angles.
The actuator systems 104 take as input power loss forecasts at
different panel orientations (provided by the prediction system
102) and provide recommendations to IoT trackers to jointly tilt
one or more solar arrays optimally ahead of time to minimize dust
deposition and maximize received irradiance. Such IoT trackers can
include IoT-enabled systems (for example, with wired or wireless
connectivity) connected with each solar panel's tracker that
records, controls and/or maintains the orientations of each
individual panel in a solar farm. The predictive IoT tracker system
102, in one or more embodiments of the invention, optimally orients
the panels in a solar farm to jointly minimize dust deposition and
maximize solar irradiance incident on the panel (that is, minimize
power losses).
By way merely of illustration, consider the following example. Let
the predicted panel dust parameters for the forecast horizon t=1, .
. . , n be:
D.sub..theta..sub.1.sub.,.theta..sub.2.sup.t={{circumflex over
(d)}.sub.1,.theta..sub.1.sub.,.theta..sub.2.sup.t, . . . ,
{circumflex over (d)}.sub.k,.theta..sub.1.sub.,.theta..sub.2.sup.t}
t=1, . . . , n, wherein .theta..sub.1 represents then tilt angle of
a solar panel, wherein {circumflex over
(d)}.sub.k,.theta..sub.1.sub.,.theta..sub.2.sup.t represents dust
deposition on the k.sup.th solar panel, and wherein .theta..sub.2
represents the rotation/orientation of a solar panel, and wherein
D.sub..theta..sub.1.sub.,.theta.2.sup.t also depends on
D.sub..theta..sub.1.sub.,.theta.2.sup.t-1, . . . ,
D.sub..theta..sub.1.sub.,.theta.2.sup.t-h;
I.sub..theta..sub.1.sub.,.theta.2.sup.t t=1, . . . , n, which
denotes the irradiance forecast at different panel orientations for
the next n time steps;
{circumflex over (P)}.sub..theta..sub.1.sub.,.theta.2.sup.t=f
({circumflex over
(D)}.sub..theta..sub.1.sub.,.theta.2.sup.t,I.sub..theta..sub.1.sub.,.thet-
a.2.sup.t), which denotes the forecasted power losses at different
panel orientations for the next n time steps, as output by the
proposed system; and
.theta..theta..theta..theta..times..times..theta..theta.
##EQU00001## which gives the optimal panel orientations for the
next n time steps.
In one or more embodiments of the invention, model predictive
control over successive moving windows of size n can be used to
continually update panel orientations to benefit from the
availability of more recent data. Additionally, multiple angles can
be supported based on the cost of the mechanical equipment.
Further, in at least one embodiment of the invention, an actuation
system can encompass and/or incorporate multiple trackers working
together. For example, consider
.theta..theta..theta..theta..times..di-elect
cons..times..theta..theta. ##EQU00002## which gives the optimal
panel orientations for all arrays .alpha. A in the solar farm for
the next n time steps. That is, the tilt of each array is not
independent, but depends on the tilts of the other arrays in the
solar farm as well.
Also, in one or more embodiments of the invention, the orientations
may differ for different sets of arrays such that trackers in the
solar farm work jointly to minimize the overall dust deposition and
maximize yield. For instance, arrays in an outer layer of the solar
farm may implement a higher tilt, which can block portions of dust
deposition for arrays within inner layers of the solar farm.
Additionally, if the predictive IoT tracker system 102 is deployed
incrementally, then arrays with this feature can support one or
more arrays without the tracker. Further, an optimization such as
detailed herein is able to support such a feature because the
prediction system 102 predicts losses in a solar farm by taking
into account the farm layout and tilts in a computational fluid
dynamics (CFD) model.
Also, in at least one embodiment of the invention, wake effects
across panels are considered to jointly orient solar arrays so as
to minimize the overall deposition of dust across the solar farm. A
wake effect on solar panels can be determined, for example, via a
modification in the incident wind speed and/or direction
accordingly in the flux of dust. Such an effect can be incorporated
via a CFD simulation, which can take the farm layouts and wind data
as inputs, and compute the variation in the wind at each individual
panel.
FIG. 2 is a diagram illustrating dust prediction components,
according to an exemplary embodiment of the invention. By way of
illustration, FIG. 2 depicts vertical dust flux data 202, aerosol
optical depth (AOD) data 204 (representing dust concentration in
the atmosphere, and based on a global chemical transport model), PV
cleaning schedule data 206, and weather data 208. As used herein,
vertical dust flux data (or, simply, vertical flux data) refer to
the component of the dust that has reached the panel or within a
close vicinity of the panel (such as, for example, the ground
directly below the panel). With respect to AOD data 204, dust in
the atmosphere can be transported worldwide by winds, covering vast
distances. At least one embodiment of the invention includes
utilizing AOD forecasts from a dust transport model to account for
an impact of long-range atmospheric dust deposition on the panels
in a solar farm. Also, with respect to the vertical dust flux data
202, at least one embodiment of the invention includes using
location data, physics-based dust dispersion models, and a CFD
model to compute the vertical dust flux density with respect to
individual arrays in a solar farm.
As also illustrated, FIG. 2 additionally depicts historical image
data 210 of the panels of the solar farm at different tilts, in
conjunction with input data 202, 204, 206, and 208, provided to a
dust deposition estimator 212, which carries out model training and
prediction via one or more machine learning models. The outputs of
the dust deposition estimator 212 can include one or more
forecasted panel dust parameters (at different tilts) 216. Such
parameters 216 can then be provided, in conjunction with historical
power output data (associated with the solar farm) 214 and incident
irradiance data at different tilts and related weather data 222, to
a dust power loss estimator 218, which carries out model training
and prediction via one or more machine learning models). The
outputs of the dust power loss estimator 218 can include one or
more forecasted power losses due to dust at different panel tilt
orientations/configurations 220.
FIG. 3 is a diagram illustrating dust prediction components,
according to an exemplary embodiment of the invention. By way of
illustration, FIG. 3 depicts weather forecast data 302, which
includes wind forecasts, precipitation forecasts, temperature
forecasts, and humidity forecasts. Such weather data 302, in
conjunction with a PV wind-blown dust model (and CFD model) 308, is
provided to a prediction component 304, which implements the PV
wind-blown dust model to generate an output. Such an output
includes a forecasted vertical dust flux 306 at a given panel
height (measured, for example, in micrograms per cubic meter). With
respect to the PV wind-blown dust model 308, such a model can
include location and/or anthropogenic data which include land use
data, surface properties, etc. Such a model can also include
ambient dust source information, and panel data (such as solar farm
layout, height information, frame information, PV surface
information, etc.).
In connection with FIG. 3, one or more embodiments of the invention
can include implementing the following example equation:
F.sub.d=E.sub.F.times.(1-R.sub.F).times.C.times.u.sub.*.sup.3.times.H,
wherein F.sub.d represents vertical dust flux, E.sub.F represents
the fraction of erodible lands capable of emitting dust, R.sub.F
represents the reduction factor for different types of lands (for
example, 0.1 for barren land), C represents a parameter value
corresponding to different soil types (for example, sandy soil,
silt, clay soil, etc.), u.sub.*.sup.3 represents a surface
frictional velocity based on wind speed and surface roughness, and
H represents the Heaviside function based on a surface frictional
velocity difference (u.sub.*-u.sub.*t). Also, in one or more
embodiments of the invention, if aerosol measurements are available
in connection with the solar farm in question, then parameters of
the physical models depicted in FIG. 3 can also be obtained via
model training on historical data.
FIG. 4 is a diagram illustrating dust prediction components,
according to an exemplary embodiment of the invention. By way of
illustration, FIG. 4 depicts a CFD model 402, which receives, as
input, forecasted wind speed and direction data at a solar farm
level, and farm layout and array tilt information from arrays 404a,
404b, and 404c (collectively, 404). The CFD model 402, based on
these inputs, outputs an estimated wind speed at each individual
array (404).
Additionally, the wind speed and direction incident on individual
arrays 404 in a solar farm can be a function of the joint layout of
all arrays and their tilts based on the farm level winds. By
modeling the farm layout and array tilts, the CFD model 402 can
translate incident wind speed and direction into per array wind
speed and direction. Based on these wind speeds and directions, the
vertical dust flux is obtained on a per array/panel level in the
solar farm (and one or more embodiments of the invention can
include combining arrays into blocks to reduce computational
complexity).
FIG. 5 is a diagram illustrating elements for forecasting solar
photovoltaic module dust, according to an exemplary embodiment of
the invention. By way of illustration, FIG. 5 depicts a training
model based on historical data, which include historical panel dust
parameters 502 derived from images, and a selected forecasted panel
dust parameter 510. Additionally FIG. 5 depicts input data that
include historical measurements and/or hindcast data 504 (such as,
for example, AOD data, humidity data, wind-blown dust data, flux
data, cleaning schedule data, precipitation data, etc.), which
include one or more features 506, and a predicted horizon 508 based
on available forecast data. Such features 506 can include, for
example, the type of precipitations, a fixed additional pollution
source from a neighboring factory, shading from trees at specific
times of day, etc.
FIG. 6 is a flow diagram illustrating techniques according to an
embodiment of the present invention. Step 602 includes deriving,
with respect to one or more solar photovoltaic modules, multiple
dust parameters from image data. The multiple dust parameters can
include dust type, data pertaining to interactions between (i) dust
on the one or more solar photovoltaic modules and (ii)
precipitation, etc.
Step 604 includes estimating, for a given future time at a current
orientation of each of the one or more solar photovoltaic modules,
(i) an amount of surface area of the one or more solar photovoltaic
modules that will be covered by dust and (ii) a yield loss of the
one or more solar photovoltaic modules associated with dust
coverage, wherein said estimating is based on the multiple dust
parameters and one or more items of input data. The one or more
items of input data can include input data from one or more
Internet of Things devices, ambient weather forecast data, one or
more surrounding soil parameters, forecasted wind speed data,
precipitation data, ambient dust forecast data (including, for
example, one or more local dust transport models and one or more
global dust transport models), location data associated with the
one or more solar photovoltaic modules (derived, for example, from
asset and/or geographic information system (GIS) data, maps,
municipal data, aerosol sensor data, etc.), cleaning schedule data
associated with the one or more solar photovoltaic modules
(derived, for example, from work order data), and/or historical
output data associated with the one or more solar photovoltaic
modules (derived, for example, from a supervisory control and data
acquisition (SCADA) system).
Step 606 includes forecasting, for the given future time at each of
one or more modified orientations for each of the one or more solar
photovoltaic modules, (i) an amount of surface area of the one or
more solar photovoltaic modules that will be covered by dust and
(ii) a yield loss of the one or more solar photovoltaic modules
associated with dust coverage, wherein said forecasting is based on
the multiple dust parameters, the one or more items of input data,
and one or more machine learning techniques. The one or more
modified orientations can include a modified tilt angle of at least
one of the one or more solar photovoltaic modules, and/or a
modified rotation position of at least one of the one or more solar
photovoltaic modules.
Step 608 includes generating an instruction to change the
orientation of at least one of the one or more solar photovoltaic
modules, prior to the given future time, based on said estimating
and said forecasting. In at least one embodiment of the invention,
the instruction reduces dust deposition on the one or more solar
photovoltaic modules and increases received irradiance by the one
or more solar photovoltaic modules. Step 610 includes outputting
the instruction to at least one actuation system associated with
the one or more solar photovoltaic modules.
The techniques depicted in FIG. 6 can also include limiting
orientation modifications for at least one of the one or more solar
photovoltaic module based on one or more temporally-based rules
associated with the given future time.
Additionally, an additional embodiment of the invention includes a
solar photovoltaic module and one or more configurable reflective
surfaces that (i) collect direct solar radiation and diffuse solar
radiation and (ii) distribute the collected direct solar radiation
and the collected diffuse solar radiation across one or more
portions of the solar photovoltaic module. Also, in such an
embodiment, each one of the plurality of configurable reflective
surfaces is physically connected to the solar photovoltaic module
at an angle that is variable in relation to the surface of the
solar photovoltaic module, and at least one variable pertaining to
each one of the plurality of configurable reflective surfaces is
configurable, wherein the at least one variable relates to
reflective surface orientation. Additionally, such an embodiment
includes a controller, wherein said controller comprises at least a
memory and a processor coupled to the memory, and wherein the
controller modulates an amount of thermal output and/or electrical
power output generated by the solar photovoltaic module, in
response to at least one forecast pertaining to (i) an amount of
surface area of the solar photovoltaic module that will be covered
by dust at a given future time in connection with multiple
configurations of the at least one variable and (ii) a yield loss
of the one or more solar photovoltaic modules associated with dust
coverage, by transmitting a signal to adjust the at least one
variable in response to the at least one forecast.
The techniques depicted in FIG. 6 can also, as described herein,
include providing a system, wherein the system includes distinct
software modules, each of the distinct software modules being
embodied on a tangible computer-readable recordable storage medium.
All of the modules (or any subset thereof) can be on the same
medium, or each can be on a different medium, for example. The
modules can include any or all of the components shown in the
figures and/or described herein. In an embodiment of the invention,
the modules can run, for example, on a hardware processor. The
method steps can then be carried out using the distinct software
modules of the system, as described above, executing on a hardware
processor. Further, a computer program product can include a
tangible computer-readable recordable storage medium with code
adapted to be executed to carry out at least one method step
described herein, including the provision of the system with the
distinct software modules.
Additionally, the techniques depicted in FIG. 6 can be implemented
via a computer program product that can include computer useable
program code that is stored in a computer readable storage medium
in a data processing system, and wherein the computer useable
program code was downloaded over a network from a remote data
processing system. Also, in an embodiment of the invention, the
computer program product can include computer useable program code
that is stored in a computer readable storage medium in a server
data processing system, and wherein the computer useable program
code is downloaded over a network to a remote data processing
system for use in a computer readable storage medium with the
remote system.
An embodiment of the invention or elements thereof can be
implemented in the form of an apparatus including a memory and at
least one processor that is coupled to the memory and configured to
perform exemplary method steps.
Additionally, an embodiment of the present invention can make use
of software running on a computer or workstation. With reference to
FIG. 7, such an implementation might employ, for example, a
processor 702, a memory 704, and an input/output interface formed,
for example, by a display 706 and a keyboard 708. The term
"processor" as used herein is intended to include any processing
device, such as, for example, one that includes a CPU (central
processing unit) and/or other forms of processing circuitry.
Further, the term "processor" may refer to more than one individual
processor. The term "memory" is intended to include memory
associated with a processor or CPU, such as, for example, RAM
(random access memory), ROM (read only memory), a fixed memory
device (for example, hard drive), a removable memory device (for
example, diskette), a flash memory and the like. In addition, the
phrase "input/output interface" as used herein, is intended to
include, for example, a mechanism for inputting data to the
processing unit (for example, mouse), and a mechanism for providing
results associated with the processing unit (for example, printer).
The processor 702, memory 704, and input/output interface such as
display 706 and keyboard 708 can be interconnected, for example,
via bus 710 as part of a data processing unit 712. Suitable
interconnections, for example via bus 710, can also be provided to
a network interface 714, such as a network card, which can be
provided to interface with a computer network, and to a media
interface 716, such as a diskette or CD-ROM drive, which can be
provided to interface with media 718.
Accordingly, computer software including instructions or code for
performing the methodologies of the invention, as described herein,
may be stored in associated memory devices (for example, ROM, fixed
or removable memory) and, when ready to be utilized, loaded in part
or in whole (for example, into RAM) and implemented by a CPU. Such
software could include, but is not limited to, firmware, resident
software, microcode, and the like.
A data processing system suitable for storing and/or executing
program code will include at least one processor 702 coupled
directly or indirectly to memory elements 704 through a system bus
710. The memory elements can include local memory employed during
actual implementation of the program code, bulk storage, and cache
memories which provide temporary storage of at least some program
code in order to reduce the number of times code must be retrieved
from bulk storage during implementation.
Input/output or I/O devices (including, but not limited to,
keyboards 708, displays 706, pointing devices, and the like) can be
coupled to the system either directly (such as via bus 710) or
through intervening I/O controllers (omitted for clarity).
Network adapters such as network interface 714 may also be coupled
to the system to enable the data processing system to become
coupled to other data processing systems or remote printers or
storage devices through intervening private or public networks.
Modems, cable modems and Ethernet cards are just a few of the
currently available types of network adapters.
As used herein, including the claims, a "server" includes a
physical data processing system (for example, system 712 as shown
in FIG. 7) running a server program. It will be understood that
such a physical server may or may not include a display and
keyboard.
The present invention may be a system, a method, and/or a computer
program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out
embodiments of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform embodiments of the present
invention.
Embodiments of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
It should be noted that any of the methods described herein can
include an additional step of providing a system comprising
distinct software modules embodied on a computer readable storage
medium; the modules can include, for example, any or all of the
components detailed herein. The method steps can then be carried
out using the distinct software modules and/or sub-modules of the
system, as described above, executing on a hardware processor 702.
Further, a computer program product can include a computer-readable
storage medium with code adapted to be implemented to carry out at
least one method step described herein, including the provision of
the system with the distinct software modules.
In any case, it should be understood that the components
illustrated herein may be implemented in various forms of hardware,
software, or combinations thereof, for example, application
specific integrated circuit(s) (ASICS), functional circuitry, an
appropriately programmed digital computer with associated memory,
and the like. Given the teachings of the invention provided herein,
one of ordinary skill in the related art will be able to
contemplate other implementations of the components of the
invention.
Additionally, it is understood in advance that implementation of
the teachings recited herein are not limited to a particular
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any type of
computing environment now known or later developed.
For example, cloud computing is a model of service delivery for
enabling convenient, on-demand network access to a shared pool of
configurable computing resources (for example, networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (for example, country,
state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (for example,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser (for
example, web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (for example, host
firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (for example, mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (for
example, cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 8, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 includes
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 8 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 9, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 8) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 9 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75. In one example, management
layer 80 may provide the functions described below. Resource
provisioning 81 provides dynamic procurement of computing resources
and other resources that are utilized to perform tasks within the
cloud computing environment. Metering and Pricing 82 provide cost
tracking as resources are utilized within the cloud computing
environment, and billing or invoicing for consumption of these
resources.
In one example, these resources may include application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and cognitive
IoT solar photovoltaic module dust predicting 96, in accordance
with the one or more embodiments of the present invention.
The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a," "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, steps,
operations, elements, and/or components, but do not preclude the
presence or addition of another feature, step, operation, element,
component, and/or group thereof.
At least one embodiment of the present invention may provide a
beneficial effect such as, for example, determining, using machine
learning techniques, power losses of solar arrays at different tilt
angles in connection with dust deposition.
The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *
References